1G Roadmap to scaling the use and adoption of AI and machine learning in Swedish water utilities

Project aim and objectives

This project aims to enhance the adoption and use of AI and machine learning tools in Swedish water utilities for asset management of water infrastructure stock, by evaluating current implementations, reviewing best practices, and leveraging expert insights.

Despite the acknowledged and reported benefits of AI and machine learning approaches there appears to be a gap in their widespread adoption in practice. Closing this gap is also critical for digital transformation in many water utilities. Some of the aspects that affect this adoption gap that project intends to focus on include:

• Data security classification: Certain types of data are highly sensitive within water utilities necessitating stringent security classifications. However, this classification often creates a barrier to implementing AI/ML solutions due to uncertainties about permissible data usage. Municipalities are often unsure about what data they can and cannot utilize for AI/ML applications without breaching confidentiality.
• Rapid technological advancements: The rapid evolution of AI/ML presents ongoing challenges in determining which algorithms, tools, and best practices to adopt or avoid. This dynamic landscape makes it difficult for water utilities to tailor these solutions to their specific needs, particularly in terms of how to integrate them effectively with existing systems and infrastructure.
• Analysis of both successful and unsuccessful applications of AI/machine learning, include assessing the reasons why and how.

Stakeholders

Swedish water utilities and VA- organizations and researchers related to decision support in sustainable water and wastewater infrastructure management. Also developers of decision support tools that assist VA organizations in making informed decisions.

Societal impact

A well-functioning water and wastewater system is essential for societal well-being. The project’s outcomes will contribute to more sustainable infrastructure management, leading to better resource utilization and improved environmental impacts.